Bramlet Matthew, Mohamadi Salman, Srinivas Jayishnu, Dassanayaka Tehan, Okammor Tafara, Shadden Mark, Sutton Bradley P
University of Illinois College of Medicine at Peoria, Pediatric Cardiology, Peoria, Illinois, United States.
University of Illinois Urbana Champaign, Bioengineering, Champaign, Illinois, United States.
J Med Imaging (Bellingham). 2024 May;11(3):034503. doi: 10.1117/1.JMI.11.3.034503. Epub 2024 May 29.
PURPOSE: Aortic dissection carries a mortality as high as 50%, but surgical palliation is also fraught with morbidity risks of stroke or paralysis. As such, a significant focus of medical decision making is on longitudinal aortic diameters. We hypothesize that three-dimensional (3D) modeling affords a more efficient methodology toward automated longitudinal aortic measurement. The first step is to automate the measurement of manually segmented 3D models of the aorta. We developed and validated an algorithm to analyze a 3D segmented aorta and output the maximum dimension of minimum cross-sectional areas in a stepwise progression from the diaphragm to the aortic root. Accordingly, the goal is to assess the diagnostic validity of the 3D modeling measurement as a substitute for existing 2D measurements. APPROACH: From January 2021 to June 2022, 66 3D non-contrast steady-state free precession magnetic resonance images of aortic pathology with clinical aortic measurements were identified; 3D aorta models were manually segmented. A novel mathematical algorithm was applied to each model to generate maximal aortic diameters from the diaphragm to the root, which were then correlated to clinical measurements. RESULTS: With a 76% success rate, we analyzed the resulting 50 3D aortic models utilizing the automated measurement tool. There was an excellent correlation between the automated measurement and the clinical measurement. The intra-class correlation coefficient and -value for each of the nine measured locations of the aorta were as follows: sinus of valsalva, 0.99, ; sino-tubular junction, 0.89, ; ascending aorta, 0.97, ; brachiocephalic artery, 0.96, ; transverse segment 1, 0.89, ; transverse segment 2, 0.93, ; isthmus region, 0.92, ; descending aorta, 0.96, ; and aorta at diaphragm, 0.3, . CONCLUSIONS: Automating diagnostic measurements that appease clinical confidence is a critical first step in a fully automated process. This tool demonstrates excellent correlation between measurements derived from manually segmented 3D models and the clinical measurements, laying the foundation for transitioning analytic methodologies from 2D to 3D.
目的：主动脉夹层的死亡率高达50%，但手术缓解也充满了中风或瘫痪的发病风险。因此，医疗决策的一个重要重点是主动脉的纵向直径。我们假设三维（3D）建模为自动纵向主动脉测量提供了一种更有效的方法。第一步是自动测量手动分割的主动脉3D模型。我们开发并验证了一种算法，用于分析3D分割的主动脉，并从膈肌逐步到主动脉根部输出最小横截面积的最大尺寸。因此，目标是评估3D建模测量作为现有二维测量替代方法的诊断有效性。 方法：从2021年1月至2022年6月，识别出66例具有临床主动脉测量值的主动脉病变的3D非对比稳态自由进动磁共振图像；手动分割3D主动脉模型。将一种新颖的数学算法应用于每个模型，以生成从膈肌到根部的最大主动脉直径，然后将其与临床测量值进行关联。 结果：我们利用自动测量工具分析了由此得到的50个3D主动脉模型，成功率为76%。自动测量与临床测量之间存在极好的相关性。主动脉九个测量位置的组内相关系数和p值如下：主动脉瓣窦，0.99，[此处p值缺失]；窦管交界处，0.89，[此处p值缺失]；升主动脉，0.97，[此处p值缺失]；头臂动脉，0.96，[此处p值缺失]；横段1，0.89，[此处p值缺失]；横段2，0.93，[此处p值缺失]；峡部区域，0.92，[此处p值缺失]；降主动脉，0.96，[此处p值缺失]；膈肌处主动脉，0.3，[此处p值缺失]。 结论：使诊断测量自动化并增强临床信心是全自动过程的关键第一步。该工具证明了从手动分割的3D模型得出的测量值与临床测量值之间具有极好的相关性，为将分析方法从二维转换为三维奠定了基础。
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